Apr 28, 2025

US-1: Full AI Nationalization can cause Misaligned Economic Incentives

Ryan Tovcimak, Nikolay Radev

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Details

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Summary

The escalating geostrategic importance of frontier AI development increases the likelihood of nationalization. While no explicit plans have emerged in the United States, such action would likely be swift and comprehensive. A government seizure of critical AI infrastructure would fundamentally transform the sector's economic foundation – shifting funding from traditional private sources to the American tax base, thereby repositioning AI as a public good. The objectives driving development would similarly pivot from user engagement to national security imperatives. Given the history of American adversaries pursuing intellectual property theft, this transition would likely establish a more restrictive diffusion model that prioritizes security over openness. By tightly controlling crucial elements of the AI stack, that approach risks diminishing the broader societal benefits that might otherwise emerge from AI advancement.

Cite this work:

@misc {

title={

US-1: Full AI Nationalization can cause Misaligned Economic Incentives

},

author={

Ryan Tovcimak, Nikolay Radev

},

date={

4/28/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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Joel Christoph

The paper offers a timely policy analysis of a full United States nationalization of frontier AI labs and argues that such a move could create misaligned economic incentives that slow diffusion and reduce overall welfare. It surveys historical precedents like the USRA, Manhattan Project, Apollo Program, MITI, and Korea’s heavy-industry drive, then applies public-choice theories such as Niskanen’s budget maximizing bureaucracy and Kornai’s soft budget constraint to foresee cost overruns and efficiency losses. The narrative is well structured and the prose is clear. The inclusion of concrete channels like talent retention, compute commandeering under the Defense Production Act, and security driven restrictions on collaboration grounds the discussion in plausible mechanisms. The historical vignettes and theories are drawn together coherently and the paper ends with pragmatic recommendations that nationalization should be a last resort in favor of “soft” public-private control. ​

The contribution is mainly descriptive and lacks formal modeling or new empirical evidence. No quantitative framework is provided to compare nationalized and private incentive structures, nor are there back-of-the-envelope fiscal estimates beyond citing past GDP percentages for the Manhattan and Apollo projects. The historical cases are summarized but not tested for external validity in the AI context. Recent literature on compute governance, state capacity in technology races, and AI alignment economics is largely missing, so the intellectual foundation rests on a limited set of classic public-choice sources.

AI safety relevance is present but indirect. The paper stresses that misaligned incentives under nationalization could hinder diffusion and perhaps heighten safety risks, yet it does not trace how a public monopoly would affect catastrophic misuse probabilities, alignment R&D funding, or global compute races. A more explicit mapping from ownership structure to safety outcomes would strengthen the impact.

Technical quality and documentation are modest. The essay is properly referenced and the parsed PDF contains tables and a Bloomberg chart, but no data, code, or appendices accompany the narrative, making replication or further analysis impossible. The policy recommendations are sensible yet untested and rely on qualitative reasoning alone.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.